Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T11:55:59.815Z Has data issue: false hasContentIssue false

THE SPECTRAL APPROACH TO LINEAR RATIONAL EXPECTATIONS MODELS

Published online by Cambridge University Press:  12 November 2024

Majid M. Al-Sadoon*
Affiliation:
Durham University Business School
*
Address correspondence to Majid M. Al-Sadoon, Department of Economics, Durham University Business School, Durham, UK; e-mail: [email protected].
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper considers linear rational expectations models in the frequency domain. The paper characterizes existence and uniqueness of solutions to particular as well as generic systems. The set of all solutions to a given system is shown to be a finite-dimensional affine space in the frequency domain. It is demonstrated that solutions can be discontinuous with respect to the parameters of the models in the context of nonuniqueness, invalidating mainstream frequentist and Bayesian methods. The ill-posedness of the problem motivates regularized solutions with theoretically guaranteed uniqueness, continuity, and even differentiability properties.

Type
ARTICLES
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

Thanks are due to Todd Walker, Bernd Funovits, Mauro Bambi, Piotr Zwiernik, Abderrahim Taamouti, Benedikt Pötscher, three anonymous referees, and seminar participants at Heriot-Watt University, Universitat Pompeu Fabra, University of Bologna, and Aarhus University.

References

REFERENCES

Al-Sadoon, M. M. (2018). The linear systems approach to linear rational expectations models. Econometric Theory, 34(3), 628658.CrossRefGoogle Scholar
Al-Sadoon, M. M. (2020). Regularized solutions to linear rational expectations models. Preprint, arXiv:2009.05875.Google Scholar
Al-Sadoon, M. M., & Zwiernik, P. (2019). The identification problem for linear rational expectations models. Preprint, arXiv:1908.09617.Google Scholar
Anderson, B. (1985). Continuity of the spectral factorization operation. Matemática Aplicada e Computacional, 4(2), 139156.Google Scholar
Arnold, V. I. (1973). Ordinary differential equations. MIT Press.Google Scholar
Baggio, G., & Ferrante, A. (2016). On the factorization of rational discrete-time spectral densities. IEEE Transactions on Automatic Control, 61(4), 969981.CrossRefGoogle Scholar
Bianchi, F., & Nicolò, G. (2021). A generalized approach to indeterminacy in linear rational expectations models. Quantitative Economics, 12(3), 843868.CrossRefGoogle Scholar
Bingham, N. (2012a). Multivariate prediction and matrix Szegö theory. Probability Surveys, 9, 325339.CrossRefGoogle Scholar
Bingham, N. (2012b). Szegö’s theorem and its probabilistic descendants. Probability Surveys, 9, 287324.CrossRefGoogle Scholar
Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods (2nd ed.). Springer.CrossRefGoogle Scholar
Broze, L., Gourieroux, C., & Szafarz, A. (1985). Solutions of linear rational expectations models. Econometric Theory, 1(3), 341368.CrossRefGoogle Scholar
Broze, L., Gouriéroux, C., & Szafarz, A. (1995). Solutions of multivariate rational expectations models. Econometric Theory, 11, 229257.CrossRefGoogle Scholar
Cagan, P. (1956). The monetary dynamics of hyperinflation. In Friedman, M. (Ed.), Studies in the quantity theory of money (pp. 25117). University of Chicago Press.Google Scholar
Caines, P. E. (1988). Linear stochastic systems. Society for Industrial and Applied Mathematics (reprinted 2018).Google Scholar
Callon, G., & Groetsch, C. (1987). The method of weighting and approximation of restricted pseudosolutions. Journal of Approximation Theory, 51(1), 1118.CrossRefGoogle Scholar
Canova, F. (2011). Methods for applied macroeconomic research. Princeton University Press.CrossRefGoogle Scholar
Chahrour, R., & Jurado, K. (2021). Recoverability and expectations-driven fluctuations. The Review of Economic Studies, 89(1), 214239.CrossRefGoogle Scholar
Christiano, L. J., & Vigfusson, R. J. (2003). Maximum likelihood in the frequency domain: The importance of time-to-plan. Journal of Monetary Economics, 50(4), 789815.CrossRefGoogle Scholar
Clancey, K. F., & Gohberg, I. (1981). Factorization of matrix functions and singular integral operators. Operator Theory: Advances and Applications (Vol. 3). Birkhäuser.CrossRefGoogle Scholar
Cramér, H. (1940). On the theory of stationary random processes. Annals of Mathematics, 41, 215230.CrossRefGoogle Scholar
Cramér, H. (1942). On harmonic analysis in certain functional spaces. Arkiv för Matematik, Astronomi och Fysik, 28B(12), Article 17.Google Scholar
Deistler, M., & Pötscher, B. M. (1984). The behaviour of the likelihood function for ARMA models. Advances in Applied Probability, 16(4), 843866.CrossRefGoogle Scholar
DeJong, D., & Dave, C. (2011). Structural macroeconometrics (2nd ed.). Princeton University Press.CrossRefGoogle Scholar
Ephremidze, L., Shargorodsky, E., & Spitkovsky, I. (2020). Quantitative results on continuity of the spectral factorization mapping. Journal of the London Mathematical Society, 101(1), 6081.CrossRefGoogle Scholar
Fanelli, L. (2012). Determinacy, indeterminacy and dynamic misspecification in linear rational expectations models. Journal of Econometrics, 170(1), 153163.CrossRefGoogle Scholar
Farmer, R. E., Khramov, V., & Nicolò, G. (2015). Solving and estimating indeterminate DSGE models. Journal of Economic Dynamics and Control, 54(C), 1736.CrossRefGoogle Scholar
Farmer, R. E. A. (1999). Macroeconomics of self-fulfilling prophecies (2nd ed.). MIT University Press.Google Scholar
Funovits, B. (2017). The full set of solutions of linear rational expectations models. Economics Letters, 161, 4751.CrossRefGoogle Scholar
Funovits, B. (2020). The dimension of the set of causal solutions of linear multivariate rational expectations models. Preprint, arXiv:2002.04369.Google Scholar
Gabaix, X. (2014). A sparsity-based model of bounded rationality. The Quarterly Journal of Economics, 129(4), 16611710.CrossRefGoogle Scholar
Galí, J. (2015). Monetary policy, inflation, and the business cycle: An introduction to the new Keynesian framework and its applications (2nd ed.). Princeton University Press.Google Scholar
Gohberg, I., Goldberg, S., & Kaashoek, M. A. (1990). Classes of linear operators (vol. 1). Birkhäuser Verlag.CrossRefGoogle Scholar
Gohberg, I., Goldberg, S., & Kaashoek, M. A. (1993). Classes of linear operators (vol. 2). Birkhäuser Verlag.CrossRefGoogle Scholar
Gohberg, I., Goldberg, S., & Kaashoek, M. A. (2003a). Basic classes of linear operators. Berkäuser Verlag.CrossRefGoogle Scholar
Gohberg, I., Kaashoek, M. A., & Spitkovsky, I. M. (2003b). An overview of matrix factorization theory and operator applications. In Gohberg, I., Manojlovic, N., & dos Santos, A. F. (Eds.), Factorization and integrable systems: Summer school in Faro, Portugal, September 2000. Operator Theory: Advances and Applications (vol. 141), chapter 1 (pp. 1102). Springer Basel AG.CrossRefGoogle Scholar
Gohberg, I. C., & Fel’dman, I. A. (1974). Convolution equations and projection methods for their solution. Translations of Mathematical Monographs (vol. 41). American Mathematical Society.Google Scholar
Green, M., & Anderson, B. D. (1987). On the continuity of the Wiener–Hopf factorization operation. The Journal of the Australian Mathematical Society. Series B. Applied Mathematics, 28(4), 443461.CrossRefGoogle Scholar
Groetsch, C. (1977). Generalized inverses of linear operators: Representation and approximation. Monographs and Textbooks in Pure and Applied Mathematics. Marcel Dekker, Inc. Google Scholar
Hadamard, J. (1902). Sur les problèmes aux dérivées partielles et leur signification physique. Princeton University Bulletin, 13, 4952.Google Scholar
Hannan, E. J. (1973). The asymptotic theory of linear time-series models. Journal of Applied Probability, 10(1), 130145.CrossRefGoogle Scholar
Hannan, E. J., & Deistler, M. (1988). The statistical theory of linear systems. Classics in Applied Mathematics. Society for Industrial and Applied Mathematics (reprinted 2012).Google Scholar
Hansen, L. P., & Sargent, T. J. (1980). Formulating and estimating dynamic linear rational expectations models. Journal of Economic Dynamics and Control, 2, 746.CrossRefGoogle Scholar
Herbst, E. P., & Schorfheide, F. (2016). Bayesian estimation of DSGE models. Princeton University Press.CrossRefGoogle Scholar
Hodrick, R. J., & Prescott, E. C. (1997). Postwar US business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 116.CrossRefGoogle Scholar
Horn, R. A., & Johnson, C. R. (1985). Matrix analysis. Cambridge University Press.CrossRefGoogle Scholar
Kailath, T. (1980). Linear systems. Prentice Hall.Google Scholar
Kalman, R., Arbib, M., & Falb, P. (1969). Topics in mathematical system theory. International Series in Pure and Applied Mathematics. McGraw-Hill.Google Scholar
Keynes, J. M. (1936). The general theory of employment, interest, and money. Palgrave Macmillan (reprinted 2018).Google Scholar
Knight, F. H. (1921). Risk, uncertainty and profit. Hart, Schaffner & Marx Prize Essays. Houghton Mifflin.Google Scholar
Kociecki, A., & Kolasa, M. (2018). Global identification of linearized DSGE models. Quantitative Economics, 9(3), 12431263.CrossRefGoogle Scholar
Kociecki, A., & Kolasa, M. (2023). A solution to the global identification problem in DSGE models. Journal of Econometrics, 236(2), Article 105477.CrossRefGoogle Scholar
Koliha, J. J. (2001). Continuity and differentiability of the Moore–Penrose inverse in C*-algebras. Mathematica Scandinavica, 88(1), 154160.CrossRefGoogle Scholar
Kolmogorov, A. N. (1939). Sur l’interpolation et extrapolation des suites stationnaires. Comptes rendus de l’Académie des Sciences Paris, 208, 20432045.Google Scholar
Kolmogorov, A. N. (1941a). Interpolation and extrapolation of stationary random sequences. Izvestiya Akademii Nauk SSSR Seriya Matematicheskaya, 5, 314.Google Scholar
Kolmogorov, A. N. (1941b). Stationary sequences in hilbert space. Moscow University Mathematics Bulletin, 2(6), 140.Google Scholar
Komunjer, I., & Ng, S. (2011). Dynamic identification of dynamic stochastic general equilibrium models. Econometrica, 79(6), 19952032.Google Scholar
Lindquist, A., & Picci, G. (2015). Linear stochastic systems: A geometric approach to modeling, estimation, and identification. Series in Contemporary Mathematics (vol. 1). Springer-Verlag.CrossRefGoogle Scholar
Locker, J., & Prenter, P. (1980). Regularization with differential operators. I. General theory. Journal of Mathematical Analysis and Applications, 74(2), 504529.CrossRefGoogle Scholar
Lubik, T. A., & Schorfheide, F. (2003). Computing sunspot equilibria in linear rational expectations models. Journal of Economic Dynamics and Control, 28(2), 273285.CrossRefGoogle Scholar
McCallum, B. T. (1983). On non-uniqueness in rational expectations models: An attempt at perspective. Journal of Monetary Economics, 11(2), 139168.CrossRefGoogle Scholar
Meyer-Gohde, A., & Tzaawa-Krenzler, M. (2023). Sticky information and the Taylor rule [Technical report]. IMFS Working Paper Series.Google Scholar
Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica, 29(3), 315335.CrossRefGoogle Scholar
Newey, W. K., & McFadden, D. (1994). Large sample estimation and hypothesis testing. In Handbook of econometrics(volume 4, chapter 36, pp. 21112245). Elsevier.CrossRefGoogle Scholar
Nikolski, N. K. (2002). Operators, functions, and systems: An easy reading: Volume 1: Hardy, Hankel, and Toeplitz. Mathematical Surveys and Monographs (vol. 92). American Mathematical Society.Google Scholar
Onatski, A. (2006). Winding number criterion for existence and uniqueness of equilibrium in linear rational expectations models. Journal of Economic Dynamics and Control, 30(2), 323345.CrossRefGoogle Scholar
Pesaran, M. H. (1987). The limits to rational expectations. Basil Blackwell Inc. Google Scholar
Pötscher, B. M., & Prucha, I. R. (1997). Dynamic nonlinear econometric models: Asymptotic theory. Springer.CrossRefGoogle Scholar
Pourahmadi, M. (2001). Foundations of time series analysis and prediction theory. John Wiley & Sons.Google Scholar
Qu, Z., & Tkachenko, D. (2012). Identification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models. Quantitative Economics, 3(1), 95132.CrossRefGoogle Scholar
Qu, Z., & Tkachenko, D. (2017). Global identification in DSGE models allowing for indeterminacy. The Review of Economic Studies, 84(3), 13061345.Google Scholar
Rogosin, S., & Mishuris, G. (2016). Constructive methods for factorization of matrix-functions. IMA Journal of Applied Mathematics, 81(2), 365391.CrossRefGoogle Scholar
Rotnitzky, A., Cox, D. R., Bottai, M., & Robins, J. (2000). Likelihood-based inference with singular information matrix. Bernoulli, 6(2), 243284.CrossRefGoogle Scholar
Rozanov, Y. A. (1967). Stationary random processes. Holden-Day.Google Scholar
Rudin, W. (1986). Real and complex analysis (3rd ed.). McGraw-Hill.Google Scholar
Sala, L. (2015). DSGE models in the frequency domains. Journal of Applied Econometrics, 30(2), 219240.CrossRefGoogle Scholar
Sargent, T. J. (1979). Macroeconomic theory. Academic Press.Google Scholar
Sims, C. A. (2002). Solving linear rational expectations models. Computational Economics, 20(1), 120.CrossRefGoogle Scholar
Sims, C. A. (2007). On the genericity of the winding number criterion for linear rational expectations models [Mimeo].Google Scholar
Smets, F., & Wouters, R. (2007). Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review, 97(3), 586606.CrossRefGoogle Scholar
Sontag, E. D. (1998). Mathematical control theory: Deterministic finite dimensional systems (2nd ed.). Texts in Applied Mathematics. Springer Verlag. www.math.rutgers.edu/∼sontag/ CrossRefGoogle Scholar
Sorge, M. (2019). Arbitrary initial conditions and the dimension of indeterminacy in linear rational expectations models. Decisions in Economics and Finance, 43, 363372.CrossRefGoogle Scholar
Sundaram, R. K. (1996). A first course in optimization theory. Cambridge University Press.CrossRefGoogle Scholar
Tan, F. (2019). A frequency-domain approach to dynamic macroeconomic models. Macroeconomic Dynamics, 25(6), 13811411.CrossRefGoogle Scholar
Tan, F., & Walker, T. B. (2015). Solving generalized multivariate linear rational expectations models. Journal of Economic Dynamics and Control, 60, 95111.CrossRefGoogle Scholar
Taylor, J. B. (1977). Conditions for unique solutions in stochastic macroeconomic models with rational expectations. Econometrica, 45(6), 13771385.CrossRefGoogle Scholar
Whiteman, C. H. (1983). Linear rational expectations models: A user’s guide. University of Minnesota Press.CrossRefGoogle Scholar
Wiener, N., & Hopf, E. (1931). Über eine klasse singulärer integralgleichungen. Sitzungsberichte der Königlich Preussischen Akademie der Wissenschaften zu Berlin, 31, 696706.Google Scholar
Wold, H. (1938). A study in the analysis of stationary time series. Almqvist and Wiksell.Google Scholar